We have presented CaDDN, a novel monocular 3D object detection method that estimates accurate categorical depth distributions for each pixel. The depth distributions are combined with the image features to generate bird's-eye-view representations that retain depth confidence, to be exploited for 3D object detection. We have shown that estimating sharp categorical distributions centered around the correct depth value, and jointly performing depth estimation and object detection is vital for 3D object detection performance, leading to a 1 place ranking on the KITTI dataset [1]} among all published methods at the time of submission.


Additional Results
KITTI Dataset Results
<TABLE>Table REF  shows the results of CaDDN on the KITTI [2]} test set for BEV detection. Our method outpeforms previous single frame methods by large margins on \(\mathrm {AP}|_{R_{40}}\)  of +2.91%, +1.59%, and +2.22% on the Car class on the Easy, Moderate, and Hard difficulties respectively. Our method outperforms the previous state-of-the art method on the Pedestrian class MonoPair [3]} with margins on \(\mathrm {AP}|_{R_{40}}\)  of +3.73%, +2.37%, and +1.88%. Our method achieves first or second place on the Cyclist class with margins on \(\mathrm {AP}|_{R_{40}}\)  of -1.37%, -1.33%, and +0.18% relative to MonoPSR [4]}.

Ablation Studies
<TABLE><TABLE>Depth Discretization.
Table REF  shows the detection peformance of CaDDN with each of the depth discretization methods outlined in Section REF . We observe that LID offers the highest performance, leading to its use for our method.
Feature Resolution.
Table REF  shows the detection peformance of CaDDN when modifying the image feature extraction layer in the Image Backbone (See Figure REF ). Experiment REF  shows the performance when image features are extracted from Block1. Experiments REF , REF , and REF  shows reduced performance when smaller resolution image features are extracted. Smaller spatial resolutions in the image features cause oversampling in the frustum to voxel grid transformation, leading to many voxel features \(\mathbf {V}\)  with similar features (See Section REF ).

Additional Details
Depth Distributions.
We add an depth bin to our depth distributions \(\mathbf {D}\)  that represents any depth outside the range \([d_{min}, d_{max}]\) . The added bin is included in the depth distribution loss \(L_\mathrm {depth}\) , but is removed when generating frustum features \(\mathbf {G}\) .
Training Details.
We initialize the Image Backbone and Depth Distribution Network (See Figure REF ) using the DeepLabV3 [5]} model pretrained on the MS-COCO dataset [6]}. All other components are randomly initialized.
